metadata

language: en
tags:
  - summarization
  - legal
  - t5
license: apache-2.0
datasets: custom

Legal Document Summarizer

This model is fine-tuned from t5-base to summarize large legal documents like constitutions and finance bills. It simplifies complex legal language, making it more accessible to non-experts.

Training Data

The model was trained on a custom dataset of legal documents and their corresponding summaries.

Intended Use

Task: Legal document summarization.
Target audience: Legal professionals, researchers, and non-experts who need quick summaries of complex legal texts.
Input: A long legal document.
Output: A concise, simplified summary.

Usage

from transformers import T5Tokenizer, T5ForConditionalGeneration

tokenizer = T5Tokenizer.from_pretrained("VincentMuriuki/legal-summarizer")
model = T5ForConditionalGeneration.from_pretrained("VincentMuriuki/legal-summarizer")

text = "Your long legal document here..."
inputs = tokenizer("summarize: " + text, return_tensors="pt", max_length=1024, truncation=True)
summary_ids = model.generate(inputs["input_ids"], max_length=150, min_length=50, length_penalty=2.0, num_beams=4, early_stopping=True)

summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
print(summary)